161 lines
7.2 KiB
Python
161 lines
7.2 KiB
Python
import asyncio
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import json
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import logging
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import os
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import time
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import traceback
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from dataclasses import asdict
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from datetime import datetime
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from typing import AsyncContextManager
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import modules.structs as structs
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import pandas as pd
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import requests
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import valkey
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from dotenv import load_dotenv
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import modules.manual_leverage as leverage
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### MANUAL LEVERAGE DATA ###
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df_leverage_by_exch = pd.DataFrame(data=leverage.LEVERAGE_BY_EXCH)
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### Database ###
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# CON: AsyncContextManager | None = None
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VAL_KEY: valkey.Valkey
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VK_OUT: str = 'fr_engine_best_fund_rate_output'
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### Logging ###
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load_dotenv()
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LOG_FILEPATH: str = f'{os.getenv(key="LOGS_PATH")}/Fund_Rate_Engine_BFR.log'
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### CONSTANTS ###
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LOOP_SLEEP_SEC: int = 5
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REFRESH_MKT_INFO_EVERY_SEC: int = 90
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REFRESH_MKT_VOLUME_EVERY_SEC: int = 30
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### GLOBALS ###
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Mkt_Info_Last_Refresh_TS_ms: int
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Mkt_Volume_Last_Refresh_TS_ms: int
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### Funcs - Load Data ###
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def get_extended_markets_info() -> pd.DataFrame:
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global Mkt_Info_Last_Refresh_TS_ms
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r: dict = json.loads(s=requests.get(url='https://api.starknet.extended.exchange/api/v1/info/markets').text)
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df: pd.DataFrame = pd.DataFrame(data=r['data'])
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df['funding_rate'] = df['marketStats'].apply(lambda x: x.get('fundingRate',{}))
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df['funding_rate_ts'] = df['marketStats'].apply(lambda x: x.get('nextFundingRate',{}))
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df['min_order_size'] = df['tradingConfig'].apply(lambda x: x.get('minOrderSize',{}))
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df['min_price_change'] = df['tradingConfig'].apply(lambda x: x.get('minPriceChange',{}))
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df['max_leverage'] = df['tradingConfig'].apply(lambda x: x.get('maxLeverage',{}))
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Mkt_Info_Last_Refresh_TS_ms = round(datetime.now().timestamp() * 1000)
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print('Extend markets info refreshed successfully')
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return df
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def load_aster_current_fr() -> pd.DataFrame:
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df = pd.DataFrame(data=json.loads(s=VAL_KEY.get(name='fund_rate_aster_all'))) # ty:ignore[invalid-argument-type]
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df: pd.DataFrame = df[['s','E','r','T']].rename({'s':'symbol','E':'funding_rate_updated_ts_ms','r':'funding_rate','T':'next_funding_ts'}, axis=1)
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df['funding_rate_updated_dt'] = pd.to_datetime(df['funding_rate_updated_ts_ms'], unit='ms')
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df['funding_rate'] = df['funding_rate'].astype(float)
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df['time_delta_to_next_funding'] = pd.to_datetime(df['next_funding_ts'], unit='ms') - pd.Timestamp.now()
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return df
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def load_extend_current_fr(df_mkt_stats: pd.DataFrame) -> pd.DataFrame:
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df = pd.DataFrame(data=json.loads(s=VAL_KEY.get(name='fund_rate_extended_all'))) # ty:ignore[invalid-argument-type]
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df: pd.DataFrame = df[['symbol','funding_rate_updated_ts_ms','funding_rate']]
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df['funding_rate_updated_dt'] = pd.to_datetime(df['funding_rate_updated_ts_ms'], unit='ms')
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df['funding_rate'] = df['funding_rate'].astype(float)
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df: pd.DataFrame = df.merge(df_mkt_stats[['name','assetName','status', 'funding_rate_ts']].rename({'name':'symbol','funding_rate_ts':'next_funding_ts'}, axis=1), on='symbol', how='left')
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df: pd.DataFrame = df.loc[df['status']=='ACTIVE',:]
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df['USDT_Symbol'] = df['assetName'] + 'USDT'
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df['time_delta_to_next_funding'] = pd.to_datetime(arg=df['next_funding_ts'], unit='ms') - pd.Timestamp.now()
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return df
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async def loop() -> None:
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global Mkt_Info_Last_Refresh_TS_ms
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df_extend_mkt_stats = get_extended_markets_info()
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try:
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while True:
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ts_arrival = round(datetime.now().timestamp() * 1000)
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if ( ts_arrival - Mkt_Info_Last_Refresh_TS_ms ) > ( REFRESH_MKT_INFO_EVERY_SEC * 1000 ):
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df_extend_mkt_stats = get_extended_markets_info()
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df_aster_fr = load_aster_current_fr()
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df_extend_fr = load_extend_current_fr(df_mkt_stats=df_extend_mkt_stats)
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df_comb_fr = df_extend_fr.merge(df_aster_fr, left_on='USDT_Symbol', right_on='symbol', how='inner', suffixes=('_ext', '_ast'))
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df_comb_fr['next_funding_at_same_time'] = (abs(df_comb_fr['time_delta_to_next_funding_ext'].dt.total_seconds() - df_comb_fr['time_delta_to_next_funding_ast'].dt.total_seconds()) / 60) < 1
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df_comb_fr['net_funding_rate'] = (df_comb_fr[['funding_rate_ext', 'funding_rate_ast']].max(axis=1) - df_comb_fr[['funding_rate_ext', 'funding_rate_ast']].min(axis=1)).where(df_comb_fr['next_funding_at_same_time'], df_comb_fr['funding_rate_ext'])
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df_comb_fr['net_funding_rate_abs'] = df_comb_fr['net_funding_rate'].abs()
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### NET MULT ###
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df_comb_fr = df_comb_fr.merge(right=df_leverage_by_exch.loc[df_leverage_by_exch['exchange']=='EXTEND'], left_on='assetName', right_on='lh_asset').merge(df_leverage_by_exch.loc[df_leverage_by_exch['exchange']=='ASTER'], left_on='assetName', right_on='lh_asset', suffixes=('_ext', '_ast'))
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df_comb_fr['net_mult'] = 1 / ( ( 0.5 / df_comb_fr['max_leverage_ext'] ) + ( 0.5 / df_comb_fr['max_leverage_ast'] ) )
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df_comb_fr['net_mult'] = df_comb_fr['net_mult'].round(2)
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df_comb_fr['net_mult_x_net_fr_abs'] = df_comb_fr['net_funding_rate_abs'] * df_comb_fr['net_mult']
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df_best_fr_rate: pd.DataFrame = df_comb_fr[['symbol_ext','symbol_ast','max_leverage_ext','max_leverage_ast','lh_asset_ext','lh_asset_ast','rh_asset_ext','rh_asset_ast','net_mult_x_net_fr_abs','net_funding_rate_abs','net_funding_rate','next_funding_at_same_time']].sort_values(by='net_mult_x_net_fr_abs', ascending=False).reset_index(drop=True)
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ASTER = structs.Perpetual_Exchange(
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mult = int(df_best_fr_rate['max_leverage_ast'][0]),
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lh_asset = df_best_fr_rate['lh_asset_ast'][0],
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rh_asset = df_best_fr_rate['rh_asset_ast'][0],
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symbol_asset_separator = '',
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)
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EXTEND = structs.Perpetual_Exchange(
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mult = int(df_best_fr_rate['max_leverage_ext'][0]),
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lh_asset = df_best_fr_rate['lh_asset_ext'][0],
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rh_asset = df_best_fr_rate['rh_asset_ext'][0],
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symbol_asset_separator = '-',
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)
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best_next_funding_pair: dict[str, dict] = {'ASTER': asdict(obj=ASTER), 'EXTEND': asdict(obj=EXTEND)}
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VAL_KEY.set(name=VK_OUT, value=json.dumps(obj=best_next_funding_pair))
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# print(best_next_funding_pair)
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time.sleep(LOOP_SLEEP_SEC)
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continue
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except valkey.exceptions.ConnectionError as e:
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logging.info(f"Could not connect to Valkey. Please check the publish server is up; {e}")
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except KeyboardInterrupt:
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logging.info('SHUTTING DOWN...')
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except Exception as e:
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logging.error(traceback.format_exc())
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logging.critical(f'*** CRASHED: {e}')
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### STARTUP ###
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async def main() -> None:
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global VAL_KEY
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# global CON
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VAL_KEY = valkey.Valkey(host='localhost', port=6379, db=0, decode_responses=True)
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# engine = create_async_engine('mysql+asyncmy://root:pwd@localhost/fund_rate')
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await loop()
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if __name__ == '__main__':
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START_TIME = round(number=datetime.now().timestamp()*1000)
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logging.info(msg=f'Log FilePath: {LOG_FILEPATH}')
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logging.basicConfig(
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force=True,
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filename=LOG_FILEPATH,
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level=logging.INFO,
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format='%(asctime)s - %(levelname)s - %(message)s',
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filemode='w'
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)
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logging.info(msg=f"STARTED: {START_TIME}")
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asyncio.run(main()) |